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            <h1 class="text-5xl font-bold">Qidong Huang</h1>
            <p class="mt-4 text-xl">Ph.D, University of Science and Technology of China</p>
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                    <a href="#home" class="text-lg font-semibold">Qidong Huang</a>
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                    <h2 class="text-3xl font-bold mb-4">Short Biography</h2>
                    <p class="text-lg leading-relaxed">Qidong Huang is a PhD student at the University of Science and Technology of China. He has published more than 7 papers at top-tier conferences and journals, such as CVPR/ICCV/AAAI/TIP/TCSVT. His research interests focus on vision transfer learning (e.g., prompt learning for vision pretrained models) and artificial intelligence security (e.g., adversarial examples and anti-DeepFake). He is the reviewer of many top conferences (including CVPR, ICCV, ECCV) and top journals (TNNLS, PR).</p>
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                    <h3 class="text-xl font-semibold">PhD of Cyberspace Security</h3>
                    <p class="text-gray-600">University of Science and Technology of China, Hefei, China, CAS Key Laboratory of Electromagnetic Space Information. Supervised by Prof. Weiming Zhang.</p>
                    <p class="text-gray-600">09/2020–present</p>
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                    <h3 class="text-xl font-semibold">Bachelor of Information Security</h3>
                    <p class="text-gray-600">School of Information Science and Technology, University of Science and Technology of China, Hefei, China.</p>
                    <p class="text-gray-600">09/2016–06/2020</p>
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                    <h3 class="text-xl font-semibold mb-4">Vision Prompt Learning</h3>
                    <p class="text-gray-600">I have been researching the prompt learning for large-scale vision pretrained models and published one paper on top-tier computer vision conferences, in which I propose DAM-VP, a data diversity-aware method for efficient and adaptive vision prompt learning. This work alleviates the mismatch between vision prompts and downstream data diversity.</p>
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                    <h3 class="text-xl font-semibold mb-4">Artificial Intelligence Security</h3>
                    <p class="text-gray-600">I have been studying artificial intelligence security since 2020, including adversarial attack&defense and anti-DeepFake. For adversarial attack, I propose SI-Adv, a shape-invariant attack for 3D point cloud recognition which great boosts the imperceptibility of adversarial examples. For adversarial defense, I propose a contrastive adversarial training framework for robust point cloud recognition named PointCAT. Besides, our work for improving adversarial robustness of masked autoencoders has been recently accepted by ICCV 2023. For anti-DeepFake, we are the first to propose the concept of “initiative defense” against DeepFakes by proactively protecting users’ facial privacy before the manipulation, unlike previous ex-post countermeasures like DeepFake detection.</p>
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                    <h3 class="text-xl font-semibold">Improving Adversarial Robustness of Masked Autoencoders via Test-time Frequency-domain Prompting</h3>
                    <p class="text-gray-600">International Conference on Computer Vision (ICCV), 2023</p>
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                    <h3 class="text-xl font-semibold">Diversity-Aware Meta Visual Prompting</h3>
                    <p class="text-gray-600">Conference on Computer Vision and Pattern Recognition (CVPR), 2023</p>
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                    <h3 class="text-xl font-semibold">Shape-invariant 3D Adversarial Point Clouds</h3>
                    <p class="text-gray-600">Conference on Computer Vision and Pattern Recognition (CVPR), 2022</p>
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                    <h3 class="text-xl font-semibold">Initiative Defense against Facial Manipulation</h3>
                    <p class="text-gray-600">AAAI Conference on Artificial Intelligence (AAAI), 2021</p>
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                    <h3 class="text-xl font-semibold">PointCAT : Contrastive Adversarial Training for Robust Point Cloud Recognition</h3>
                    <p class="text-gray-600">IEEE Transactions on Image Processing (TIP), Major Revision</p>
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                    <h3 class="text-xl font-semibold">Ada3Diff : Defending against 3D Adversarial Point Clouds via Adaptive Diffusion</h3>
                    <p class="text-gray-600">Under Review</p>
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                    <h3 class="text-xl font-semibold">Deep Template-based Watermarking</h3>
                    <p class="text-gray-600">IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2020</p>
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                    <h3 class="text-xl font-semibold">Poison ink : Robust and invisible backdoor attack</h3>
                    <p class="text-gray-600">IEEE Transactions on Image Processing (TIP), 2022</p>
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                <li>Reviewer for CVPR 2022, 2023</li>
                <li>Reviewer for ICCV 2023</li>
                <li>Reviewer for ECCV 2022</li>
                <li>Reviewer for ICPR 2022</li>
                <li>Reviewer for IEEE Transactions on Neural Networks and Learning Systems (TNNLS)</li>
                <li>Reviewer for Pattern Recognition (PR)</li>
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                    <h3 class="text-xl font-semibold">2021 China National Scholarship</h3>
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